Takafumi Kanamori

107 papers A* 4A 7B 4C 1Journal 81Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Noiseless Diffusion-GAN: Scaling-based data augmentation for generative models. Neural Networks Yoshitaka Koike, Takumi Nakagawa, Hiroki Waida, Takafumi Kanamori
2025	J	`jnl`	NAS-DG: Ultra-Fast NAS via Uncertainty Modeling for Domain Generalization. IEEE Access Keigo Wakayama, Takafumi Kanamori
2025	—	`conf`	TULiP: Test-Time Uncertainty Estimation via Linearization and Weight Perturbation. ICONIP (1) Yuhui Zhang, Dongshen Wu, Yuichiro Wada, Takafumi Kanamori
2025	J	`jnl`	TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation. CoRR Yuhui Zhang, Dongshen Wu, Yuichiro Wada, Takafumi Kanamori
2025	J	`jnl`	Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain. Neural Networks Lan Yang, Chen Qiao, Takafumi Kanamori, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang
2025	J	`jnl`	Ultra-Fast NAS Based on Normalized Generalization Error with Random NTK. IEICE Trans. Inf. Syst. Keigo Wakayama, Takafumi Kanamori
2024	J	`jnl`	Denoising cosine similarity: A theory-driven approach for efficient representation learning. Neural Networks Takumi Nakagawa, Yutaro Sanada, Hiroki Waida, Yuhui Zhang, Yuichiro Wada, Kosaku Takanashi, Tomonori Yamada, Takafumi Kanamori
2024	—	`conf`	Open-World Learning Under Dataset Shift. CAI Ponhvoan Srey, Yuhui Zhang, Takafumi Kanamori
2024	J	`jnl`	Robust Estimation for Kernel Exponential Families with Smoothed Total Variation Distances. CoRR Takafumi Kanamori, Kodai Yokoyama, Takayuki Kawashima
2024	B	`conf`	Robust VAEs via Generating Process of Noise Augmented Data. ISIT Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki, Yuhui Zhang, Takumi Nakagawa, Hiroki Waida, Yuichiro Wada, Takafumi Kanamori
2024	J	`jnl`	Robust VAEs via Generating Process of Noise Augmented Data. CoRR Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki, Yuhui Zhang, Takumi Nakagawa, Hiroki Waida, Yuichiro Wada, Takafumi Kanamori
2024	J	`jnl`	Scaling-based Data Augmentation for Generative Models and its Theoretical Extension. CoRR Yoshitaka Koike, Takumi Nakagawa, Hiroki Waida, Takafumi Kanamori
2023	J	`jnl`	A Convex Framework for Confounding Robust Inference. CoRR Kei Ishikawa, Niao He, Takafumi Kanamori
2023	J	`jnl`	Deep Clustering With a Constraint for Topological Invariance Based on Symmetric InfoNCE. Neural Comput. Yuhui Zhang, Yuichiro Wada, Hiroki Waida, Kaito Goto, Yusaku Hino, Takafumi Kanamori
2023	J	`jnl`	Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE. CoRR Yuhui Zhang, Yuichiro Wada, Hiroki Waida, Kaito Goto, Yusaku Hino, Takafumi Kanamori
2023	J	`jnl`	Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning. CoRR Takumi Nakagawa, Yutaro Sanada, Hiroki Waida, Yuhui Zhang, Yuichiro Wada, Kosaku Takanashi, Tomonori Yamada, Takafumi Kanamori
2023	J	`jnl`	Learning domain invariant representations by joint Wasserstein distance minimization. Neural Networks Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon
2023	—	`conf`	Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. ECML/PKDD (4) Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa, Yuhui Zhang, Takafumi Kanamori
2023	J	`jnl`	Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis. CoRR Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa, Yuhui Zhang, Takafumi Kanamori
2022	A	`conf`	Deep Self-Supervised Learning of Speech Denoising from Noisy Speeches. INTERSPEECH Yutaro Sanada, Takumi Nakagawa, Yuichiro Wada, Kosaku Takanashi, Yuhui Zhang, Kiichi Tokuyama, Takafumi Kanamori, Tomonori Yamada
2022	J	`jnl`	Estimating Density Models with Truncation Boundaries using Score Matching. J. Mach. Learn. Res. Song Liu, Takafumi Kanamori, Daniel J. Williams
2022	A	`conf`	Mode estimation on matrix manifolds: Convergence and robustness. AISTATS Hiroaki Sasaki, Jun-ichiro Hirayama, Takafumi Kanamori
2021	J	`jnl`	Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization. CoRR Léo Andéol, Yusei Kawakami, Yuichiro Wada, Takafumi Kanamori, Klaus-Robert Müller, Grégoire Montavon
2021	J	`jnl`	Uncertainty propagation for dropout-based Bayesian neural networks. Neural Networks Yuki Mae, Wataru Kumagai, Takafumi Kanamori
2020	A	`conf`	A Unified Statistically Efficient Estimation Framework for Unnormalized Models. AISTATS Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
2020	A	`conf`	Robust modal regression with direct gradient approximation of modal regression risk. UAI Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
2019	J	`jnl`	Estimating Density Models with Complex Truncation Boundaries. CoRR Song Liu, Takafumi Kanamori
2019	A*	`conf`	Fisher Efficient Inference of Intractable Models. NeurIPS Song Liu, Takafumi Kanamori, Wittawat Jitkrittum, Yu Chen
2019	J	`jnl`	Model Description of Similarity-Based Recommendation Systems. Entropy Takafumi Kanamori, Naoya Osugi
2019	J	`jnl`	Risk bound of transfer learning using parametric feature mapping and its application to sparse coding. Mach. Learn. Wataru Kumagai, Takafumi Kanamori
2019	J	`jnl`	Robust Label Prediction via Label Propagation and Geodesic IEICE Trans. Inf. Syst. Yuichiro Wada, Siqiang Su, Wataru Kumagai, Takafumi Kanamori
2019	J	`jnl`	Robust modal regression with direct log-density derivative estimation. CoRR Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
2019	J	`jnl`	Spectral Embedded Deep Clustering. Entropy Yuichiro Wada, Shugo Miyamoto, Takumi Nakagama, Léo Andéol, Wataru Kumagai, Takafumi Kanamori
2019	J	`jnl`	Unified estimation framework for unnormalized models with statistical efficiency. CoRR Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
2019	J	`jnl`	Variable Selection for Nonparametric Learning with Power Series Kernels. Neural Comput. Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
2018	J	`jnl`	Variable Selection for Nonparametric Learning with Power Series Kernels. CoRR Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
2017	J	`jnl`	Breakdown Point of Robust Support Vector Machines. Entropy Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2017	J	`jnl`	DC Algorithm for Extended Robust Support Vector Machine. Neural Comput. Shuhei Fujiwara, Akiko Takeda, Takafumi Kanamori
2017	A	`conf`	Estimating Density Ridges by Direct Estimation of Density-Derivative-Ratios. AISTATS Hiroaki Sasaki, Takafumi Kanamori, Masashi Sugiyama
2017	J	`jnl`	Graph-based composite local Bregman divergences on discrete sample spaces. Neural Networks Takafumi Kanamori, Takashi Takenouchi
2017	J	`jnl`	Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
2017	J	`jnl`	Parallel distributed block coordinate descent methods based on pairwise comparison oracle. J. Glob. Optim. Kota Matsui, Wataru Kumagai, Takafumi Kanamori
2017	J	`jnl`	Robustness of learning algorithms using hinge loss with outlier indicators. Neural Networks Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2017	J	`jnl`	Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences. J. Mach. Learn. Res. Takashi Takenouchi, Takafumi Kanamori
2016	J	`jnl`	Efficiency Bound of Local Z-Estimators on Discrete Sample Spaces. Entropy Takafumi Kanamori
2015	—	`conf`	Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces. NIPS Takashi Takenouchi, Takafumi Kanamori
2014	J	`jnl`	Breakdown Point of Robust Support Vector Machine. CoRR Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
2014	J	`jnl`	Constrained Least-Squares Density-Difference Estimation. IEICE Trans. Inf. Syst. Tuan Duong Nguyen, Marthinus Christoffel du Plessis, Takafumi Kanamori, Masashi Sugiyama
2014	J	`jnl`	Extended Robust Support Vector Machine Based on Financial Risk Minimization. Neural Comput. Akiko Takeda, Shuhei Fujiwara, Takafumi Kanamori
2014	J	`jnl`	Numerical study of learning algorithms on Stiefel manifold. Comput. Manag. Sci. Takafumi Kanamori, Akiko Takeda
2014	J	`jnl`	Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle. CoRR Kota Matsui, Wataru Kumagai, Takafumi Kanamori
2014	J	`jnl`	Scale-Invariant Divergences for Density Functions. Entropy Takafumi Kanamori
2014	J	`jnl`	Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy Takafumi Kanamori, Masashi Sugiyama
2014	J	`jnl`	Using financial risk measures for analyzing generalization performance of machine learning models. Neural Networks Akiko Takeda, Takafumi Kanamori
2013	J	`jnl`	A Bregman extension of quasi-Newton updates I: an information geometrical framework. Optim. Methods Softw. Takafumi Kanamori, Atsumi Ohara
2013	J	`jnl`	A Bregman extension of quasi-Newton updates II: Analysis of robustness properties. J. Comput. Appl. Math. Takafumi Kanamori, Atsumi Ohara
2013	J	`jnl`	A Unified Classification Model Based on Robust Optimization. Neural Comput. Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2013	J	`jnl`	Computational complexity of kernel-based density-ratio estimation: a condition number analysis. Mach. Learn. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2013	J	`jnl`	Conjugate relation between loss functions and uncertainty sets in classification problems. J. Mach. Learn. Res. Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2013	J	`jnl`	Density-Difference Estimation. Neural Comput. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2013	J	`jnl`	Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning. J. Comput. Sci. Eng. Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Masao Yamanaka, Makoto Yamada, Taiji Suzuki, Takafumi Kanamori
2013	J	`jnl`	Improving Logitboost with prior knowledge. Inf. Fusion Takafumi Kanamori, Takashi Takenouchi
2013	J	`jnl`	Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput. Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
2013	J	`jnl`	Semi-supervised learning with density-ratio estimation. Mach. Learn. Masanori Kawakita, Takafumi Kanamori
2013	J	`jnl`	Statistical models and learning algorithms for ordinal regression problems. Inf. Fusion Takafumi Kanamori
2012	J	`jnl`	A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems CoRR Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2012	A*	`conf`	A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems. COLT Takafumi Kanamori, Akiko Takeda, Taiji Suzuki
2012	J	`jnl`	A Unified Robust Classification Model CoRR Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2012	A*	`conf`	A Unified Robust Classification Model. ICML Akiko Takeda, Hiroyuki Mitsugi, Takafumi Kanamori
2012	—	`book`	Density Ratio Estimation in Machine Learning. Masashi Sugiyama, Taiji Suzuki, Takafumi Kanamori
2012	J	`jnl`	Density-Difference Estimation CoRR Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2012	—	`conf`	Density-Difference Estimation. NIPS Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, Ichiro Takeuchi
2012	—	`conf`	Non-convex Optimization on Stiefel Manifold and Applications to Machine Learning. ICONIP (1) Takafumi Kanamori, Akiko Takeda
2012	J	`jnl`	Statistical analysis of kernel-based least-squares density-ratio estimation. Mach. Learn. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2012	J	`jnl`	Worst-Case Violation of Sampled Convex Programs for Optimization with Uncertainty. J. Optim. Theory Appl. Takafumi Kanamori, Akiko Takeda
2012	J	`jnl`	f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models. IEEE Trans. Inf. Theory Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2011	J	`jnl`	Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search. Neural Networks Masashi Sugiyama, Makoto Yamada, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
2011	J	`jnl`	Least-squares two-sample test. Neural Networks Masashi Sugiyama, Taiji Suzuki, Yuta Itoh, Takafumi Kanamori, Manabu Kimura
2011	J	`jnl`	Multiscale Bagging and Its Applications. IEICE Trans. Inf. Syst. Hidetoshi Shimodaira, Takafumi Kanamori, Masayoshi Aoki, Kouta Mine
2011	—	`conf`	Relative Density-Ratio Estimation for Robust Distribution Comparison. NIPS Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
2011	J	`jnl`	Statistical outlier detection using direct density ratio estimation. Knowl. Inf. Syst. Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
2010	A	`conf`	Conditional Density Estimation via Least-Squares Density Ratio Estimation. AISTATS Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
2010	J	`jnl`	Deformation of log-likelihood loss function for multiclass boosting. Neural Networks Takafumi Kanamori
2010	A	`conf`	Direct Density Ratio Estimation with Dimensionality Reduction. SDM Masashi Sugiyama, Satoshi Hara, Paul von Bünau, Taiji Suzuki, Takafumi Kanamori, Motoaki Kawanabe
2010	J	`jnl`	Least-Squares Conditional Density Estimation. IEICE Trans. Inf. Syst. Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara
2010	J	`jnl`	Theoretical Analysis of Density Ratio Estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama
2009	J	`jnl`	A Density-ratio Framework for Statistical Data Processing. Inf. Media Technol. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang
2009	J	`jnl`	A Density-ratio Framework for Statistical Data Processing. IPSJ Trans. Comput. Vis. Appl. Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, Liwei Wang
2009	J	`jnl`	A Least-squares Approach to Direct Importance Estimation. J. Mach. Learn. Res. Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
2009	J	`jnl`	A robust approach based on conditional value-at-risk measure to statistical learning problems. Eur. J. Oper. Res. Akiko Takeda, Takafumi Kanamori
2009	J	`jnl`	Mutual information estimation reveals global associations between stimuli and biological processes. BMC Bioinform. Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, Jun Sese
2009	J	`jnl`	Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression. Neural Comput. Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori
2008	—	`conf`	Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation. FSDM Taiji Suzuki, Masashi Sugiyama, Jun Sese, Takafumi Kanamori
2008	—	`conf`	Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. NIPS Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
2008	A*	`conf`	Inlier-Based Outlier Detection via Direct Density Ratio Estimation. ICDM Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori
2008	J	`jnl`	Robust Boosting Algorithm Against Mislabeling in Multiclass Problems. Neural Comput. Takashi Takenouchi, Shinto Eguchi, Noboru Murata, Takafumi Kanamori
2007	B	`conf`	Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. ALT Takafumi Kanamori
2007	J	`jnl`	Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. IEICE Trans. Inf. Syst. Takafumi Kanamori
2007	J	`jnl`	Pool-based active learning with optimal sampling distribution and its information geometrical interpretation. Neurocomputing Takafumi Kanamori
2007	J	`jnl`	Robust Loss Functions for Boosting. Neural Comput. Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
2006	J	`jnl`	Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions. Comput. Stat. Data Anal. Takafumi Kanamori, Ichiro Takeuchi
2006	J	`jnl`	Geometrical Structure of Boosting Algorithm. New Gener. Comput. Takafumi Kanamori, Takashi Takenouchi, Noboru Murata
2006	B	`conf`	The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator. IJCNN Ichiro Takeuchi, Kaname Nomura, Takafumi Kanamori
2004	J	`jnl`	Information Geometry of U-Boost and Bregman Divergence. Neural Comput. Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi
2004	B	`conf`	The Most Robust Loss Function for Boosting. ICONIP Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
2002	C	`conf`	A New Sequential Algorithm for Regression Problems by Using Mixture Distribution. ICANN Takafumi Kanamori
2002	J	`jnl`	Robust Regression with Asymmetric Heavy-Tail Noise Distributions. Neural Comput. Ichiro Takeuchi, Yoshua Bengio, Takafumi Kanamori